To solve the global scale problem, [1]}, [2]} proposed to utilize stereo image pairs to recover the absolute scale of pose estimation. They introduced an additional spatial photometric loss between the left and right pairs of images, as the stereo baseline (i.e. motion transformation between the left and right images) is fixed and known throughout the dataset.
Once the training is complete, the network produces pose predictions using only monocular images. Thus, although it is unsupervised in the context of not having access to ground-truth, the training dataset (stereo) is different to the test set (mono). [3]} tackles the scale issue by introducing a geometric consistency loss, that enforces the consistency between predicted depth maps and reconstructed depth maps. The framework transforms the predicted depth maps into a 3D space, and projects them back to produce reconstructed depth maps. In doing so, the depth predictions are able to remain scale-consistent over consecutive frames, enabling pose estimates to be scale-consistent meanwhile.
